Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier

Masazhar, Ahmad Nor Ikhwan; Kamal, Mahanijah Md

doi:10.1109/icsima.2017.8311978

Cited by 39 publications

(13 citation statements)

References 2 publications

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“…They concluded that applying the principal component analysis to extract the input features for ANN by splitting the histogram of RGB, L*a*b, HIS, and HSV color spaces into eight bins provided the highest accuracy. Masazhar and Kamal (2017) developed a multi-class SVM to classify the oil palm leaf images of Anthracnose and Chimaera diseases. The K-means clustering algorithm was used to separate the region with the disease symptom from the entire image.…”

Section: Related Work Plant Disease Detection Using Conventional Mach...mentioning

confidence: 99%

“…This visual inspection is time-consuming and thus not feasible for large areas of oil palm plantations (Alaa et al, 2020). Moreover, since the analysis of oil palm disease often requires trained expertise, which is usually lacking in remote areas and small farms, the detection results are prone to human misjudgment (Masazhar & Kamal, 2017). Therefore, the daily crop health monitoring with conventional methods is less efficient for small and medium plantation farms.…”

Section: Introductionmentioning

confidence: 99%

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Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Ong

Woon

2022

JICT

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Over the years, numerous studies have been conducted on the integration of computer vision and machine learning in plant disease detection. However, these conventional machine learning methods often require the contour segmentation of the infected region from the entire leaf region and the manual extraction of different discriminative features before the classification models can be developed. In this study, deep learning models, specifically, the AlexNet convolutional neural network (CNN) and the combination of AlexNet and support vector machine (AlexNet-SVM), which overcome the limitation of handcrafting of feature representation were implemented for oil palm leaf disease identification. The images of healthy and infected leaf samples were collected, resized, and renamed before the model training. These images were directly used to fit the classification models, without the need for segmentation and feature extraction as in models, without the need for segmentation and feature extraction as in the conventional machine learning methods. The optimal architecture of AlexNet CNN and AlexNet-SVM models were then determined and subsequently applied for the oil palm leaf disease identification.Comparative studies showed that the overall performance of the AlexNet CNN model outperformed AlexNet-SVM-based classifier.

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Section: Related Work Plant Disease Detection Using Conventional Mach...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Ong

Woon

2022

JICT

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“…Another study regarding the identification of soybean plant diseases based on texture features using the Gabor method shows that recognising disease features is highly dependent on delivering the correct value of the frequency and orientation parameters [6]. Research on disease identification through the combination of textural features with GLCM and colour features in the LAB colour space can provide an accuracy of up to 97% [7].…”

Section: Literature Reviewmentioning

confidence: 99%

Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features

Rahayu¹,

Mudjirahardjo²,

Muslim³

2021

IJCATR

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Peanuts are a food crop commodity that Indonesians widely consume as a vegetable fat and protein source. However, the quality and quantity of peanut productivity may decline, one of which is due to plant diseases. Efforts that can be made to maintain peanut productivity are the application of technology to detect peanut plant diseases early; thus, disease control can be carried out earlier. This study presents a technology development application, particularly digital image processing, to identify disease features of infected peanut leaves based on GLCM texture features and colour features in the HSV colour space and classified using the SVM method. The development of the SVM method that is applied is the Multiclass SVM with the DAGSVM strategy, which can classify more than two classes. Based on the experimental results, it confirms that the combination of HSV colour features and GLCM texture features with an angular orientation of 0 degrees and classified by the Multiclass SVM method with polynomial kernels produces the highest accuracy, i.e. 99.1667% for leaf spot class, 97.5% for leaf rust class, 98.8333% for eyespot class, 100% for normal leaf class and 100% for other leaf class.

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“…Work related to Support Vector Machine algorithm was primarily demonstrated by Ahmad et. al which used image processing on palm oil leaf disease detection by using K-mean clustering and Multiclass SVM (Support Vector Machine) classifier [28]. Author had planned the methodology on image processing which start with image acquisition, contrast enhancement, conversion of RGB image to various colour spaces, K-mean clustering, feature extraction and lastly Multiclass SVM classifier.…”

Section: Study Done By Dimitris Et Al Demonstrated the Suitability Relevancy And Comparison Of Uas Image Processing Techniques With This mentioning

confidence: 99%

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

Johnathan¹,

Moh²

2021

International Journal of Innovative Research in Electrical, Ele

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Digital image processing technique for palm oil leaf disease detection using multiclass SVM classifier

Cited by 39 publications

References 2 publications

Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Image-Based Oil Palm Leaves Disease Detection Using Convolutional Neural Network

Leaf Diseases Classification on Peanut Leaves Based on Texture and Colour Features

Unmanned Aircraft System (UAS) Based Large Area Identification of Basal Stem Rot (BSR) Infected Oil Palm Tree Using Convolutional Neural Network (CNN)

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